The rapidly evolving field of Artificial Intelligence (AI) has led to significant advancements in Machine Learning (ML), with “inference” emerging as a crucial concept. But what exactly is inference, and how does it work in a way that you can make most useful for your AI-based applications?
First, Understanding Inference.
In the context of ML, inference refers to the process of utilizing a trained model to make predictions, draw conclusions, or generate text about new, unseen data. This stage is the culmination of the ML pipeline, where the model is deployed to produce outputs based on the patterns, relationships, and insights it acquired during the training phase of AI. Don’t worry, we’ll explain more later in this post. Inference is a critical step, as it enables ML models to be applied in real-world scenarios, such as:
- Language translation: Translating text from one language to another
- Text summarization: Condensing long pieces of text into concise summaries
- Sentiment analysis: Determining the emotional tone or sentiment behind a piece of text
- Image classification: Identifying objects or patterns within images
- Speech recognition: Transcribing spoken language into text
Inference is the key to unlocking the full potential of ML models, allowing them to be used in a wide range of applications, from virtual assistants and chatbots to self-driving cars and medical diagnosis systems. This is where Groq® LPU™ AI inference technology adds extra value to AI applications – end users get access to leading models running at top speeds deployed on our GroqCloud™ system at an affordable price. Examples like speech recognition will only be useful in real-time applications if the system can provide exceptionally fast inference speeds like Groq does. But, that should be affordable so developers can build layers into their applications (this is called an agentic workflow) to improve the quality of results.
Defining Inference: A Simple Analogy
Imagine you’re having a conversation with a language model, and you ask it to complete a sentence: “I love reading books about _______.” The model has been trained on a vast amount of text data, including books, articles, and conversations. Based on this training, the model uses its knowledge to make an inference: “I love reading books about science fiction.” The model didn’t simply memorize the answer; instead, it used the patterns and relationships it learned from the training data to generate a response that makes sense in the context of the sentence. That might not be the answer you would have provided but the training the model received, and the context of the interaction you are having with the Large Language Model (LLM) like a chat interface, dictates the probability of an answer.
In the example about reading books, the model’s inference is based on its understanding of language patterns, such as:
- The types of topics people often read about
- The words that are commonly associated with each topic
- The grammatical structures and sentence patterns used in language
The model’s response is its best guess, given the input it received and the knowledge it acquired during training. This process of using learned knowledge to make predictions or generate text is a fundamental aspect of inference in ML, particularly in the context of LLMs. A worthy side note – this is why models with a larger parameter size are often more capable of providing better answers. That is until recently, when Meta AI released an update to their open-source Llama 3.3 70 billion parameter sized model that nearly competes on quality compared to their 405 billion parameter Llama model. If you want to read more about that, click here.
How Inference Works
Inference involves a series of complex steps, including:
- Pattern recognition: The model recognizes patterns in the input data, such as language patterns, image features, or speech patterns.
- Knowledge retrieval: The model retrieves relevant knowledge from its training data, such as relationships between words, objects, or concepts.
- Contextual understanding: The model understands the context in which the input data is being used, such as the topic, tone, or intent behind the text.
- Prediction or generation: The model uses its knowledge and understanding to make a prediction or generate text, such as completing a sentence or translating a piece of text.
Types of Inference
There are several types of inference used in ML, including:
- Logical inference: Used logical rules and reasoning to make predictions or draw conclusions
- Statistical inference: Uses statistical models and probability theory to make predictions or estimate parameters
- Neural inference: Uses neural networks to make predictions or generate text. An LLM is an example of a transformer-based neural network.
If you want to get much deeper into this and read about the foundation of these methods check out the paper, Attention Is All You Need by Vaswani et al. from 2017 or this transformer neural network explanation video.
Real-World Applications of Inference
Inference has numerous real-world applications, including the following. Check out the examples powered by Groq:
- Virtual assistants: Using inference to understand voice commands and generate responses
- Chatbots: Using inference to understand user input and generate responses
- Voice audible interfaces: Using inference to convert speech-to-text and then transcribe that audio
- Medical diagnosis: Using inference to analyze medical images and diagnose diseases
Here are some demo applications Powered by Groq – use these to try it yourself!
- GroqChat: Try different leading AI models to compare speed and results
- Stockbot: See how the agent workflow mixed with other techniques allows you to interface with real time market data
- Stream of Thought to Text: Allows you to speak into your microphone and iterate on results to produce content in real time
A Summary of Inference To Get Started
In essence, inference is the process of applying learned knowledge to make predictions or decisions about new, unseen data. It’s a fundamental concept in ML, and understanding it is key to unlocking the full potential of AI applications. By grasping the basics of inference, you’ll be better equipped to explore the exciting world of AI and its many applications, from language translation and text summarization to image recognition and speech synthesis.
After understanding inference, a great next step is to begin exploring prompting techniques or understanding what a token is in ML.
Got questions? Join our Discord community to talk with Groqsters and GroqChamps, or watch one of our many videos on YouTube.